Nonstationary Z-Score measures

In this work we develop advanced techniques for measuring bank insolvency risk. More specifically, we contribute to the existing body of research on the Z-Score. We develop bias reduction strategies for state-of-the-art Z-Score measures in the literature. We introduce novel estimators whose aim is to effectively capture nonstationary returns; for these estimators, as well as for existing ones in the literature, we discuss analytical confidence regions. We exploit moment-based error measures to assess the effectiveness of these estimators. We carry out an extensive empirical study that contrasts state-of-the-art estimators to our novel ones on over ten thousand banks. Finally, we contrast results obtained by using Z-Score estimators against business news on the banking sector obtained from Factiva. Our work has important implications for researchers and practitioners. First, accounting for nonstationarity in returns yields a more accurate quantification of the degree of solvency. Second, our measure allows researchers to factor in the degree of uncertainty in the estimation due to the availability of data.